Are you the 1%?

The extent and reasons that might cause you to worry about semiconductor yield challenges depends on many factors. If your device is manufactured using a leading edge process, you probably work tirelessly and hand-in-hand with your foundry to ensure that process and product yield is ramped up more or less in parallel. If, however, your company is lagging behind a node or two, yield might not keep you awake at night, at least not until the unexpected happens.

For devices in markets such as medical and automotive, you would carefully look for anything that could indicate a quality or reliability concern. Then there are those we affectionately refer to the 1%-ers: those products that have been manufactured for a long enough time and in high enough volume that it makes financial sense to chase down that that last 1% yield loss.

With the increasing number of yield challenges, such as the dramatic increase in number and complexity of design sensitive defects, many fabless semiconductor companies are arming themselves with new technologies like DDYA (diagnosis-driven yield analysis), which can rapidly identify the root cause of yield loss and effectively separate design- and process-oriented yield loss. In a recently published case, Freescale used the results from diagnosis analysis of 1300 failing die to improve mature yield by 1.5% in a few weeks. New advances in diagnosis analysis technology make DDYA more valuable than ever before.

The second component in a DDYA flow is the statistical analysis, which makes the diagnosis results from a number of failing devices actionable. The primary challenge for yield analysis based on diagnosis data is dealing with the ambiguity in the results. For example, the defective behavior seen on the tester could be explained by a defect in more than just one single location. Second, each diagnosis result, often referred to as a suspect, could have multiple possible root causes associated with it.

To effectively eliminate the noise in the diagnosis results and determine the underlying root causes represented in a population of failing devices, you can use a new technology called RCD (root cause deconvolution). This technology is based on Bayesian probability analysis, which is well-known in machine learning applications.

RCD leverages design statistics such as critical area per net segment per metal layer and count of tested cells per cell type. The technology uses a probabilistic model that calculates the probability of observing a set of diagnosis results for a given defect distribution. This model is in turn applied to determine the most likely defect distribution for a given set of diagnosis results. Figure 2 (next page) shows typical RCD analysis flow.